Fatigue driving monitoring method and system based on fusion of face recognition and behavior recognition

Abstract

The invention discloses a fatigue driving monitoring method based on fusion of face recognition and behavior recognition. The method comprises the following steps of detecting the position of a humanbody for a collected behavior image; carrying out skeleton detection on the detected human body position to obtain the position of each part of the human body in the image and the corresponding confidence coefficient, predicting an association vector field among the parts at the same time, and expressing a connection relationship among the parts through the association vector field to obtain a human body skeleton model; comparing the skeleton model of the predefined fatigue driving state with the obtained skeleton model to obtain a behavior state recognition result; processing the collected facial image to obtain the facial expression state characteristics, judging whether fatigue driving exists or not according to the facial expression state characteristics, and obtaining a facial state recognition result; and fusing the face state recognition result and the behavior state recognition result to obtain a final detection result. The face recognition result and the behavior recognition result are dynamically fused, so that the fatigue driving state can be accurately judged, and the precision is higher.

Description

technical field [0001] The invention relates to the technical field of fatigue driving detection, in particular to a fatigue driving monitoring method and system based on facial recognition and behavior recognition fusion. Background technique [0002] At present, there are many research methods for the detection of driver fatigue state, which can be roughly divided into detection based on driver’s physiological signal, detection based on driver’s operation behavior, detection based on vehicle state information and detection based on driver’s physiological response. feature detection methods. [0003] The accuracy of judging fatigue driving based on physiological signals (EEG signals, ECG signals, etc.) is high, and for all healthy drivers, the physiological signals have little difference and have commonalities, but the traditional physiological signal acquisition method requires The use of contact measurement brings a lot of inconvenience and limitations to the practical a...

AI Technical Summary

Problems solved by technology

[0005] The driver's fatigue state can also be estimated by using vehicle driving state information such as vehicle trajectory changes and lane line deviations, but the driving state of the vehicle is also related to many environmental factors such as vehicle characteristics and roads, and is related to the driver's driving experience and driving habits. Therefore, there are many interference factors that need to be considered in judging fatigue driving based on vehicle state information

[0006] The fatigue driving discrimination method based on the driver's physiological response characteristics refers to the use of the driver's eye characteristics, mouth movement characteristics, etc. to infer the driver's fatigue state. Closing time and yawning actions can be directly used to detect fatigue, but due to certain differences in the habits and characteristics of different drivers, the robustness of judging the driver's state through a single facial expression feature is not high enough

[0007] However, due to the individual differences of drivers, the detection method of a single detection index has limitations, mainly manifested in low accuracy, prone to deviation, etc.

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